Using Deep Learning to Automate Eosinophil Counting in Pediatric Ulcerative Colitis Histopathological Images.

Background: Accurate identification of inflammatory cells from mucosal histopathology images is important in diagnosing ulcerative colitis. The identification of eosinophils in the colonic mucosa has been associated with disease course. Cell counting is not only time-consuming but can also be subjective to human biases. In this study we developed an automatic eosinophilic cell counting tool from mucosal histopathology images, using deep learning. Method: Four pediatric IBD pathologists from two North American pediatric hospitals annotated 530 crops from 143 standard-of-care hematoxylin and eosin (H & E) rectal mucosal biopsies. A 305/75 split was used for training/validation to develop and optimize a U-Net based deep learning model, and 150 crops were used as a test set. The U-Net model was then compared to SAU-Net, a state-of-the-art U-Net variant. We undertook post-processing steps, namely, (1) the pixel-level probability threshold, (2) the minimum number of clustered pixels to designate a cell, and (3) the connectivity. Experiments were run to optimize model parameters using AUROC and cross-entropy loss as the performance metrics. Results: The F1-score was 0.86 (95%CI:0.79-0.91) (Precision: 0.77 (95%CI:0.70-0.83), Recall: 0.96 (95%CI:0.93-0.99)) to identify eosinophils as compared to an F1-score of 0.2 (95%CI:0.13-0.26) for SAU-Net (Precision: 0.38 (95%CI:0.31-0.46), Recall: 0.13 (95%CI:0.08-0.19)). The inter-rater reliability was 0.96 (95%CI:0.93-0.97). The correlation between two pathologists and the algorithm was 0.89 (95%CI:0.82-0.94) and 0.88 (95%CI:0.80-0.94) respectively. Conclusion: Our results indicate that deep learning-based automated eosinophilic cell counting can obtain a robust level of accuracy with a high degree of concordance with manual expert annotations.

Accessible chromatin maps of inflammatory bowel disease intestine nominate cell-type mediators of genetic disease risk.

Inflammatory Bowel Disease ( IBD ) is a chronic and often debilitating autoinflammatory condition, with an increasing incidence in children. Standard-of-care therapies lead to sustained transmural healing and clinical remission in fewer than one-third of patients. For children, TNFα inhibition remains the only FDA-approved biologic therapy, providing an even greater urgency to understanding mechanisms of response. Genome-wide association studies ( GWAS ) have identified 418 independent genetic risk loci contributing to IBD, yet the majority are noncoding and their mechanisms of action are difficult to decipher. If causal, they likely alter transcription factor ( TF ) binding and downstream gene expression in particular cell types and contexts. To bridge this knowledge gap, we built a novel resource: multiome-seq (tandem single-nuclei ( sn )RNA-seq and chromatin accessibility ( snATAC )-seq) of intestinal tissue from pediatric IBD patients, where anti-TNF response was defined by endoscopic healing. From the snATAC-seq data, we generated a first-time atlas of chromatin accessibility (putative regulatory elements) for diverse intestinal cell types in the context of IBD. For cell types/contexts mediating genetic risk, we reasoned that accessible chromatin will co-localize with genetic disease risk loci. We systematically tested for significant co-localization of our chromatin accessibility maps and risk variants for 758 GWAS traits. Globally, genetic risk variants for IBD, autoimmune and inflammatory diseases are enriched in accessible chromatin of immune populations, while other traits (e.g., colorectal cancer, metabolic) are enriched in epithelial and stromal populations. This resource opens new avenues to uncover the complex molecular and cellular mechanisms mediating genetic disease risk.

Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review.

BACKGROUND: There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. METHODS: We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). RESULTS: Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images. CONCLUSION: The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.

Outcomes Following Acute Severe Colitis at Initial Presentation: A Multi-centre, Prospective, Paediatric Cohort Study.

AIM: To assess contemporary outcomes in children with acute severe ulcerative colitis [ASUC] at initial presentation. METHODS: Between April 2014 and January 2019, children aged <17 years, with new onset ASUC (Paediatric Ulcerative Colitis Activity Index [PUCAI ≥65) were prospectively followed in a Canadian inception cohort study. 16S rRNA amplicon sequencing captured microbial composition of baseline faecal samples. Primary endpoint was corticosteroid-free clinical remission with intact colon at 1 year [PUCAI <10, no steroids ≥4 weeks]. RESULTS: Of 379 children with new onset UC/IBD-unclassified, 105 [28%] presented with ASUC (42% male; median [interquartile range; [IQR]) age 14 [11-16] years; extensive colitis in all). Compared with mild UC, gut microbiome of ASUC patients had lower α-diversity, decreased beneficial anaerobes, and increased aerobes; 54 [51%] children were steroid-refractory and given infliximab [87% intensified regimen]. Corticosteroid-free remission at 1 year was achieved by 62 [61%] ASUC cohort (by 34 [63%] steroid-refractory patients, all on biologics; by 28 [55%] steroid responders,13 [25%] on 5- aminosalicylic acid [5-ASA], 5 [10%] on thiopurines, 10 [20%] on biologics). By 1 year, 78 [74%] escalated to infliximab including 24 [47%] steroid-responders failed by 5-ASA and/or thiopurines. In multivariable analysis, clinical predictors for commencing infliximab included hypoalbuminaemia, greater PUCAI, higher age, and male sex. Over 18 months, repeat corticosteroid course[s] and repeat hospitalisation were less likely among steroid-refractory versus -responsive but -dependent patients (adjusted odds ratio [aOR] 0.71 [95% CI 0.57-0.89] and 0.54 [95% CI 0.45-0.66], respectively). CONCLUSION: The majority of children presenting with ASUC escalate therapy to biologics. Predictors of need for advanced therapy may guide selection of optimal maintenance therapy.

Linc2function: A Comprehensive Pipeline and Webserver for Long Non-Coding RNA (lncRNA) Identification and Functional Predictions Using Deep Learning Approaches.

Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms. We propose that understanding the mechanisms of lncRNA in pathological processes requires solving their structural motifs and interactomes. A complete lncRNA annotation first involves discriminating them from their coding counterparts and then predicting their functional motifs and target bio-molecules. Current in silico methods mainly perform primary-sequence-based discrimination using a reference model, limiting their comprehensiveness and generalizability. We demonstrate that integrating secondary structure and interactome information, in addition to using transcript sequence, enables a comprehensive functional annotation. Annotating lncRNA for newly sequenced species is challenging due to inconsistencies in functional annotations, specialized computational techniques, limited accessibility to source code, and the shortcomings of reference-based methods for cross-species predictions. To address these challenges, we developed a pipeline for identifying and annotating transcript sequences at the isoform level. We demonstrate the effectiveness of the pipeline by comprehensively annotating the lncRNA associated with two specific disease groups. The source code of our pipeline is available under the MIT licensefor local use by researchers to make new predictions using the pre-trained models or to re-train models on new sequence datasets. Non-technical users can access the pipeline through a web server setup.

Racial Disparities in Pediatric Inflammatory Bowel Disease Care: Differences in Outcomes and Health Service Utilization Between Black and White Children.

OBJECTIVE: To describe racial inequities in pediatric inflammatory bowel disease care and explore potential drivers. METHODS: We undertook a single-center, comparative cohort study of newly diagnosed Black and non-Hispanic White patients with inflammatory bowel disease, aged <21 years, from January 2013 through 2020. Primary outcome was corticosteroid-free remission (CSFR) at 1 year. Other longitudinal outcomes included sustained CSFR, time to anti-tumor necrosis factor therapy, and evaluation of health service utilization. RESULTS: Among 519 children (89% White, 11% Black), 73% presented with Crohn's disease and 27% with ulcerative colitis. Disease phenotype did not differ by race. More patients from Black families had public insurance (58% vs 30%, P < .001). Black patients were less likely to achieve CSFR 1-year post diagnosis (OR: 0.52, 95% CI:0.3-0.9) and less likely to achieve sustained CSFR (OR: 0.48, 95% CI: 0.25-0.92). When adjusted by insurance type, differences by race to 1-year CSFR were no longer significant (aOR: 0.58; 95% CI: 0.33, 1.04; P = .07). Black patients were more likely to transition from remission to a worsened state, and less likely to transition to remission. We found no differences in biologic therapy utilization or surgical outcomes by race. Black patients had fewer gastroenterology clinic visits and 2-fold increased odds for emergency department visits. CONCLUSIONS: We observed no differences by race in phenotypic presentation and medication usage. Black patients had half the odds of achieving clinical remission, but a degree of this was mediated by insurance status. Understanding the cause of such differences will require further exploration of social determinants of health.

Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications.

The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology. Comparatively, the majority of deep learning systems developed in inflammatory bowel disease have focused on predicting disease severity and were developed using still images rather than videos. The application of AI to pediatric endoscopy is in its infancy, thus providing an opportunity to develop clinically meaningful and fair systems that do not perpetuate societal biases. In this review, we provide an overview of AI, summarize the advances of AI in endoscopy, and describe its potential application to pediatric endoscopic practice and education.

STR-based feature extraction and selection for genetic feature discovery in neurological disease genes.

Gene expression, often determined by single nucleotide polymorphisms, short repeated sequences known as short tandem repeats (STRs), structural variants, and environmental factors, provides means for an organism to produce gene products necessary to live. Variation in expression levels, sometimes known as enrichment patterns, has been associated with disease progression. Thus, the STR enrichment patterns have recently gained interest as potential genetic markers for disease progression. However, to the best of our knowledge, we are unaware of any study that evaluates and explores STRs, particularly trinucleotide sequences, as machine learning features for classifying neurological disease genes for the purpose of discovering genetic features. Thus, in this paper, we proposed a new metric and a novel feature extraction and selection algorithm based on statistically significant STR-based features and their respective enrichment patterns to create a statistically significant feature set. The proposed new metric has shown that the neurological disease family genes have a non-random AA, AT, TA, TG, and TT enrichment pattern. This is an important result, as it supports prior research that has established that certain trinucleotides, such as AAT, ATA, ATT, TAT, and TTA, are favored during protein misfolding. In contrast, trinucleotides, such as TAA, TAG, and TGA, are favored during premature termination codon mutations as they are stop codons. This suggests that the metric has the potential to identify patterns that may be genetic features in a sample of neurological genes. Moreover, the practical performance and high prediction results of the statistically significant STR-based feature set indicate that variations in STR enrichment patterns can distinguish neurological disease genes. In conclusion, the proposed approach may have the potential to discover differential genetic features for other diseases.

Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach.

BACKGROUND: The malaria risk prediction is currently limited to using advanced statistical methods, such as time series and cluster analysis on epidemiological data. Nevertheless, machine learning models have been explored to study the complexity of malaria through blood smear images and environmental data. However, to the best of our knowledge, no study analyses the contribution of Single Nucleotide Polymorphisms (SNPs) to malaria using a machine learning model. More specifically, this study aims to quantify an individual's susceptibility to the development of malaria by using risk scores obtained from the cumulative effects of SNPs, known as weighted genetic risk scores (wGRS). RESULTS: We proposed an SNP-based feature extraction algorithm that incorporates the susceptibility information of an individual to malaria to generate the feature set. However, it can become computationally expensive for a machine learning model to learn from many SNPs. Therefore, we reduced the feature set by employing the Logistic Regression and Recursive Feature Elimination (LR-RFE) method to select SNPs that improve the efficacy of our model. Next, we calculated the wGRS of the selected feature set, which is used as the model's target variables. Moreover, to compare the performance of the wGRS-only model, we calculated and evaluated the combination of wGRS with genotype frequency (wGRS + GF). Finally, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Ridge regression algorithms are utilized to establish the machine learning models for malaria risk prediction. CONCLUSIONS: Our proposed approach identified SNP rs334 as the most contributing feature with an importance score of 6.224 compared to the baseline, with an importance score of 1.1314. This is an important result as prior studies have proven that rs334 is a major genetic risk factor for malaria. The analysis and comparison of the three machine learning models demonstrated that LightGBM achieves the highest model performance with a Mean Absolute Error (MAE) score of 0.0373. Furthermore, based on wGRS + GF, all models performed significantly better than wGRS alone, in which LightGBM obtained the best performance (0.0033 MAE score).

Leveraging Mann-Whitney U test on large-scale genetic variation data for analysing malaria genetic markers.

BACKGROUND: The malaria risk analysis of multiple populations is crucial and of great importance whilst compressing limitations. However, the exponential growth in diversity and accumulation of genetic variation data obtained from malaria-infected patients through Genome-Wide Association Studies opens up unprecedented opportunities to explore the significant differences between genetic markers (risk factors), particularly in the resistance or susceptibility of populations to malaria risk. Thus, this study proposes using statistical tests to analyse large-scale genetic variation data, comprising 20,854 samples from 11 populations within three continents: Africa, Oceania, and Asia. METHODS: Even though statistical tests have been utilized to conduct case-control studies since the 1950s to link risk factors to a particular disease, several challenges faced, including the choice of data (ordinal vs. non-ordinal) and test (parametric vs. non-parametric). This study overcomes these challenges by adopting the Mann-Whitney U test to analyse large-scale genetic variation data; to explore the statistical significance of markers between populations; and to further identify the highly differentiated markers. RESULTS: The findings of this study revealed a significant difference in the genetic markers between populations (p < 0.01) in all the case groups and most control groups. However, for the highly differentiated genetic markers, a significant difference (p < 0.01) was present for most genetic markers with varying p-values between the populations in the case and control groups. Moreover, several genetic markers were observed to have very significant differences (p < 0.001) across all populations, while others exist between certain specific populations. Also, several genetic markers have no significant differences between populations. CONCLUSIONS: These findings further support that the genetic markers contribute differently between populations towards malaria resistance or susceptibility, thus showing differences in the likelihood of malaria infection. In addition, this study demonstrated the robustness of the Mann-Whitney U test in analysing genetic markers in large-scale genetic variation data, thereby indicating an alternative method to explore genetic markers in other complex diseases. The findings hold great promise for genetic markers analysis, and the pipeline emphasized in this study can fully be reproduced to analyse new data.

Prioritising positively selected variants in whole-genome sequencing data using FineMAV.

BACKGROUND: In population genomics, polymorphisms that are highly differentiated between geographically separated populations are often suggestive of Darwinian positive selection. Genomic scans have highlighted several such regions in African and non-African populations, but only a handful of these have functional data that clearly associates candidate variations driving the selection process. Fine-Mapping of Adaptive Variation (FineMAV) was developed to address this in a high-throughput manner using population based whole-genome sequences generated by the 1000 Genomes Project. It pinpoints positively selected genetic variants in sequencing data by prioritizing high frequency, population-specific and functional derived alleles. RESULTS: We developed a stand-alone software that implements the FineMAV statistic. To graphically visualise the FineMAV scores, it outputs the statistics as bigWig files, which is a common file format supported by many genome browsers. It is available as a command-line and graphical user interface. The software was tested by replicating the FineMAV scores obtained using 1000 Genomes Project African, European, East and South Asian populations and subsequently applied to whole-genome sequencing datasets from Singapore and China to highlight population specific variants that can be subsequently modelled. The software tool is publicly available at https://github.com/fadilla-wahyudi/finemav . CONCLUSIONS: The software tool described here determines genome-wide FineMAV scores, using low or high-coverage whole-genome sequencing datasets, that can be used to prioritize a list of population specific, highly differentiated candidate variants for in vitro or in vivo functional screens. The tool displays these scores on the human genome browsers for easy visualisation, annotation and comparison between different genomic regions in worldwide human populations.

Incidence of Inflammatory Bowel Disease in South Asian and Chinese People: A Population-Based Cohort Study from Ontario, Canada.

BACKGROUND: Inflammatory bowel disease (IBD) is now a global disease with incidence increasing throughout Asia. AIM: To determine the incidence of IBD among South Asians and Chinese people residing in Ontario, Canada's most populous province. METHODS: All incident cases of IBD in children (1994-2015) and adults (1999-2015) were identified from population-based health administrative data. We classified South Asian and Chinese ethnicity using immigration records and surnames. We determined standardized incidence of IBD and adjusted incidence rate ratio (aIRR) in South Asians and Chinese compared to the general population. RESULTS: Among 16,230,638 people living in Ontario, standardized incidence of IBD per 100,000 person-years was 24.7 (95% CI 24.4-25.0), compared with 14.6 (95% CI 13.7-15.5) in 982,472 South Asians and with 5.4 (95% CI 4.8-5.9) in 764,397 Chinese. The risk of IBD in South Asians was comparable to the general population after adjusting for immigrant status and confounders (aIRR 1.03, 95% CI 0.96-1.10). South Asians had a lower risk of Crohn's disease (CD) (aIRR 0.66, 95% CI 0.60-0.77), but a higher risk of ulcerative colitis (UC) (aIRR 1.47, 95% CI 1.34-1.61). Chinese people had much lower rates of IBD (aIRR 0.24, 95% CI 0.20-0.28), CD (aIRR 0.21, 95% CI 0.17-0.26), and UC (aIRR 0.28, 95% CI 0.23-0.25). CONCLUSION: Canadians of South Asian ethnicity had a similarly high risk of developing IBD compared to other Canadians, and a higher risk of developing UC, a finding distinct from the Chinese population. Our findings indicate the importance of genetic and environmental risk factors in people of Asian origin who live in the Western world.

One-year outcomes with ustekinumab therapy in infliximab-refractory paediatric ulcerative colitis: a multicentre prospective study.

BACKGROUND: The phase 3 (UNIFI) trial of ustekinumab (anti-interleukin 12/23) demonstrated efficacy even after prior biologic failure in adult ulcerative colitis (UC), but paediatric data are lacking. AIM: To prospectively monitor efficacy and serum concentrations of ustekinumab given to children with UC refractory to other biologics. METHODS: Children with anti-TNF refractory UC initiating ustekinumab intravenously at sites of the Canadian Children IBD Network prior to 12/2019 are included. The primary endpoint was steroid-free clinical remission with subcutaneous ustekinumab at 52 weeks (Paediatric Ulcerative Colitis Activity Index <10, no steroids ≥4 weeks). Ustekinumab levels were measured after week 20. Endoscopic improvement was defined as Mayo endoscopic subscore ≤1, or faecal calprotectin (FCP) <250 µg/g if not re-colonoscoped. RESULTS: At six sites between 01/2018 and 11/2019, 25 children (median [IQR] age 14.8 years [12.3-16.2], 72% female) with UC duration 2.3 years (1.1-4.2) received intravenous ustekinumab (median dose/kg of 6.4 [5.5-7.5] mg). All patients had failed prior infliximab therapy, and 12 (48%) also vedolizumab. Five patients discontinued ustekinumab after IV induction (four undergoing colectomy). On intent to treat basis, 44% achieved the primary endpoint of steroid-free remission at week 52, including nine (69%) of 13 who previously treated with anti-TNF only vs two (17%) of 12 who previously failed also by vedolizumab (P = 0.008). Seven of 11 remitters met the criteria for endoscopic improvement. The median (IQR) trough levels (µg/mL) were greater with q4 vs q8 weekly dosing (8.7 [4.6-9.9] vs 3.8 [12.7-4.8]) P = 0.02, but greater exposure was not associated with a superior rate of clinical remission. No adverse events were associated with therapy. CONCLUSION: Ustekinumab demonstrated efficacy in this paediatric cohort with otherwise treatment-refractory UC. Treatment failure was not due to inadequate drug exposure.

Predicting Therapeutic Response in Pediatric Ulcerative Colitis-A Journey Towards Precision Medicine.

Ulcerative colitis (UC) is a disabling disease, characterized by chronic inflammation of the colon, with a rising prevalence worldwide in the pediatric age group. Although UC presents in children with varying severity, disease extent, and comorbidities, initial treatment is essentially uniform, consisting of 5-aminosalicylate drugs with corticosteroid induction for those with moderately to severely active disease. With the advent of anti-tumor necrosis factor (TNF) biologic therapy and several new biologics and small-molecule drugs for UC, precision medicine approaches to treatment are needed to more rapidly achieve sustained remission, restore quality of life, normalize development, and limit exposure to toxic corticosteroids in children with UC. Here, we review available data on clinical, biochemical, histopathologic, and molecular predictors of treatment response in UC. We also address known predictors and special treatment considerations in specific relevant scenarios such as very-early-onset UC, acute severe UC, ileal pouch anal anastomosis, and UC with concomitant primary sclerosing cholangitis. The review concludes with a prediction of how machine learning will integrate multimodal patient data to bring precision medicine to the bedside of children with UC in the future.

Accurate Classification of Pediatric Colonic Inflammatory Bowel Disease Subtype Using a Random Forest Machine Learning Classifier.

BACKGROUND: The pediatric inflammatory bowel disease (PIBD) classes algorithm was developed to bring consistency to labelling of colonic IBD, but labels are exclusively based on features atypical for ulcerative colitis (UC). AIM: The aim of the study was to develop an algorithm and identify features that discriminate between pediatric UC and colonic Crohn disease (CD). METHODS: Baseline clinical, endoscopic, radiologic, and histologic data, including the PIBD class features in 74 colonic IBD (56: UC, 18: colonic CD) patients were collected. The PIBD class features and additional features common to UC were used to perform initial clustering, using similarity network fusion (SNF). We trained a Random Forest (RF) classifier on the full dataset and used a leave-one-out approach to evaluate model accuracy. The top-features were used to build a new classifier, which we tested on 15 previously unused patients. We then performed clustering with SNF on the top RF features and assessed ability to discriminate between UC and colonic-CD independent of a supervised model. RESULTS: The initial SNF clustering with 58 patients demonstrated 2 groups: group 1 (n = 39, 90% UC) and group 2 (n = 19, 68% colonic-CD). Our RF classifier correctly labelled 97% of the 58 patients based on leave-one-out cross validation and identified the 7 most important features (3 histological and 4 endoscopic) to clinically distinguish these groups. We trained a new RF classifier with the top 7 features and found 100% accuracy in a set of 15 held-out patients. Finally, post hoc clustering with these 7 features revealed 2 groups of patients: group 1 (n = 55, 98% UC) and group 2 (n = 18, 94% colonic-CD). CONCLUSIONS: A combination of supervised and unsupervised analyses identified a short list of features, which consistently distinguish UC from colonic CD. Future directions include validation in other populations.

STRIDE-II: An Update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): Determining Therapeutic Goals for Treat-to-Target strategies in IBD.

BACKGROUND: The Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) initiative of the International Organization for the Study of Inflammatory Bowel Diseases (IOIBD) has proposed treatment targets in 2015 for adult patients with inflammatory bowel disease (IBD). We aimed to update the original STRIDE statements for incorporating treatment targets in both adult and pediatric IBD. METHODS: Based on a systematic review of the literature and iterative surveys of 89 IOIBD members, recommendations were drafted and modified in 2 surveys and 2 voting rounds. Consensus was reached if ≥75% of participants scored the recommendation as 7 to 10 on a 10-point rating scale. RESULTS: In the systematic review, 11,278 manuscripts were screened, of which 435 were included. The first IOIBD survey identified the following targets as most important: clinical response and remission, endoscopic healing, and normalization of C-reactive protein/erythrocyte sedimentation rate and calprotectin. Fifteen recommendations were identified, of which 13 were endorsed. STRIDE-II confirmed STRIDE-I long-term targets of clinical remission and endoscopic healing and added absence of disability, restoration of quality of life, and normal growth in children. Symptomatic relief and normalization of serum and fecal markers have been determined as short-term targets. Transmural healing in Crohn's disease and histological healing in ulcerative colitis are not formal targets but should be assessed as measures of the remission depth. CONCLUSIONS: STRIDE-II encompasses evidence- and consensus-based recommendations for treat-to-target strategies in adults and children with IBD. This frameworkshould be adapted to individual patients and local resources to improve outcomes.

Selected neuropeptide genes show genetic differentiation between Africans and non-Africans.

BACKGROUND: Publicly available genome data provides valuable information on the genetic variation patterns across different modern human populations. Neuropeptide genes are crucial to the nervous, immune, endocrine system, and physiological homeostasis as they play an essential role in communicating information in neuronal functions. It remains unclear how evolutionary forces, such as natural selection and random genetic drift, have affected neuropeptide genes among human populations. To date, there are over 100 known human neuropeptides from the over 1000 predicted peptides encoded in the genome. The purpose of this study is to analyze and explore the genetic variation in continental human populations across all known neuropeptide genes by examining highly differentiated SNPs between African and non-African populations. RESULTS: We identified a total of 644,225 SNPs in 131 neuropeptide genes in 6 worldwide population groups from a public database. Of these, 5163 SNPs that had ΔDAF |(African - non-African)| ≥ 0.20 were identified and fully annotated. A total of 20 outlier SNPs that included 19 missense SNPs with a moderate impact and one stop lost SNP with high impact, were identified in 16 neuropeptide genes. Our results indicate that an overall strong population differentiation was observed in the non-African populations that had a higher derived allele frequency for 15/20 of those SNPs. Highly differentiated SNPs in four genes were particularly striking: NPPA (rs5065) with high impact stop lost variant; CHGB (rs6085324, rs236150, rs236152, rs742710 and rs742711) with multiple moderate impact missense variants; IGF2 (rs10770125) and INS (rs3842753) with moderate impact missense variants that are in linkage disequilibrium. Phenotype and disease associations of these differentiated SNPs indicated their association with hypertension and diabetes and highlighted the pleiotropic effects of these neuropeptides and their role in maintaining physiological homeostasis in humans. CONCLUSIONS: We compiled a list of 131 human neuropeptide genes from multiple databases and literature survey. We detect significant population differentiation in the derived allele frequencies of variants in several neuropeptide genes in African and non-African populations. The results highlights SNPs in these genes that may also contribute to population disparities in prevalence of diseases such as hypertension and diabetes.

Body Composition Using Air Displacement Plethysmography in Children With Inflammatory Bowel Disease.

OBJECTIVE: The aim of the study was to assess the body composition of children with inflammatory bowel disease (IBD) and to study the accuracy of clinically available tools in predicting excess body fatness. We aimed at also exploring the influence of adiposity on pharmacokinetics during early Infliximab exposure. METHODS: Prospective cohort study in 5- to 17-year-old children with IBD initiating Infliximab therapy. Patient demographic, phenotypic, and laboratory data at the time of Infliximab initiation were recorded. Body composition was assessed using air displacement plethysmography (ADP). fat mass index (FMI = fat mass [kg]/(height [m])) was calculated to determine excess adiposity (defined as FMI ≥75th centile). Anthropometrics (weight, height, mid upper arm circumference [MUAC] and triceps skin fold thickness [TSF]) were obtained and MUAC and TSF measurements were used to calculate arm fat area (AFA) and arm muscle area z-scores. Statistical analysis was applied as appropriate. RESULTS: Fifty-three (68% male; 55% Crohn disease [CD], 45% ulcerative colitis [UC], median [IQR] age 15 [13-16] years) children with IBD were included. Twenty-four percentage of children with IBD (21% CD, 29% UC) had excess adiposity. Four children (31%) with FMI ≥75th centile were not identified by body mass index (BMI) alone (kappa of 0.60), and 2 children (15%) were not identified by AFA z-score alone. The intra- and interobserver reliability of MUAC and TSFT measurements was excellent. There was no difference in Infliximab trough levels at the end of induction between those with FMI less than or ≥75th centile. CONCLUSIONS: Excess adiposity affects approximately 1 in 4 young patients with IBD and can be missed by routine obesity screening. Our exploratory study did not raise concerns of underexposure to infliximab in those children with excess adiposity during early drug exposure.